237 research outputs found
Improving Hurricane Power Outage Prediction Models Through the Inclusion of Local Environmental Factors
Tropical cyclones can significantly damage the electrical power system, so an accurate spatiotemporal forecast of outages prior to landfall can help utilities to optimize the power restoration process. The purpose of this article is to enhance the predictive accuracy of the Spatially Generalized Hurricane Outage Prediction Model (SGHOPM) developed by Guikema et al. (2014). In this version of the SGHOPM, we introduce a new twoâstep prediction procedure and increase the number of predictor variables. The first model step predicts whether or not outages will occur in each location and the second step predicts the number of outages. The SGHOPM environmental variables of Guikema et al. (2014) were limited to the wind characteristics (speed and duration of strong winds) of the tropical cyclones. This version of the model adds elevation, land cover, soil, precipitation, and vegetation characteristics in each location. Our results demonstrate that the use of a new twoâstep outage prediction model and the inclusion of these additional environmental variables increase the overall accuracy of the SGHOPM by approximately 17%.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147200/1/risa12728_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147200/2/risa12728.pd
A regional Bayesian POT model for flood frequency analysis
Flood frequency analysis is usually based on the fitting of an extreme value
distribution to the local streamflow series. However, when the local data
series is short, frequency analysis results become unreliable. Regional
frequency analysis is a convenient way to reduce the estimation uncertainty. In
this work, we propose a regional Bayesian model for short record length sites.
This model is less restrictive than the index flood model while preserving the
formalism of "homogeneous regions". The performance of the proposed model is
assessed on a set of gauging stations in France. The accuracy of quantile
estimates as a function of the degree of homogeneity of the pooling group is
also analysed. The results indicate that the regional Bayesian model
outperforms the index flood model and local estimators. Furthermore, it seems
that working with relatively large and homogeneous regions may lead to more
accurate results than working with smaller and highly homogeneous regions
Estimation of conditional laws given an extreme component
Let be a bivariate random vector. The estimation of a probability of
the form is challenging when is large, and a
fruitful approach consists in studying, if it exists, the limiting conditional
distribution of the random vector , suitably normalized, given that
is large. There already exists a wide literature on bivariate models for which
this limiting distribution exists. In this paper, a statistical analysis of
this problem is done. Estimators of the limiting distribution (which is assumed
to exist) and the normalizing functions are provided, as well as an estimator
of the conditional quantile function when the conditioning event is extreme.
Consistency of the estimators is proved and a functional central limit theorem
for the estimator of the limiting distribution is obtained. The small sample
behavior of the estimator of the conditional quantile function is illustrated
through simulations.Comment: 32 pages, 5 figur
Understanding extreme sea levels for broad-scale coastal impact and adaptation analysis
One of the main consequences of mean sea level rise (SLR) on human settlements is an increase in flood risk due to an increase in the intensity and frequency of extreme sea levels (ESL). While substantial research efforts are directed towards quantifying projections and uncertainties of future global and regional SLR, corresponding uncertainties in contemporary ESL have not been assessed and projections are limited. Here we quantify, for the first time at global scale, the uncertainties in present-day ESL estimates, which have by default been ignored in broad-scale sea-level rise impact assessments to date. ESL uncertainties exceed those from global SLR projections and, assuming that we meet the Paris agreement goals, the projected SLR itself by the end of the century in many regions. Both uncertainties in SLR projections and ESL estimates need to be understood and combined to fully assess potential impacts and adaptation needs
Contrasting responses of mean and extreme snowfall to climate change
Snowfall is an important element of the climate system, and one that is expected to change in a warming climate. Both mean snowfall and the intensity distribution of snowfall are important, with heavy snowfall events having particularly large economic and human impacts. Simulations with climate models indicate that annual mean snowfall declines with warming in most regions but increases in regions with very low surface temperatures. The response of heavy snowfall events to a changing climate, however, is unclear. Here I show that in simulations with climate models under a scenario of high emissions of greenhouse gases, by the late twenty-first century there are smaller fractional changes in the intensities of daily snowfall extremes than in mean snowfall over many Northern Hemisphere land regions. For example, for monthly climatological temperatures just below freezing and surface elevations below 1,000 metres, the 99.99th percentile of daily snowfall decreases by 8% in the multimodel median, compared to a 65% reduction in mean snowfall. Both mean and extreme snowfall must decrease for a sufficiently large warming, but the climatological temperature above which snowfall extremes decrease with warming in the simulations is as high as â9 °C, compared to â14 °C for mean snowfall. These results are supported by a physically based theory that is consistent with the observed rainâsnow transition. According to the theory, snowfall extremes occur near an optimal temperature that is insensitive to climate warming, and this results in smaller fractional changes for higher percentiles of daily snowfall. The simulated changes in snowfall that I find would influence surface snow and its hazards; these changes also suggest that it may be difficult to detect a regional climate-change signal in snowfall extremes.National Science Foundation (U.S.) (Grant AGS-1148594)United States. National Aeronautics and Space Administration (ROSES Grant 09-IDS09-0049
Measuring and Modeling Risk Using High-Frequency Data
Measuring and modeling financial volatility is the key to derivative pricing, asset allocation and risk management. The recent availability of high-frequency data allows for refined methods in this field. In particular, more precise measures for the daily or lower frequency volatility can be obtained by summing over squared high-frequency returns. In turn, this so-called realized volatility can be used for more accurate model evaluation and description of the dynamic and distributional structure of volatility. Moreover, non-parametric measures of systematic risk are attainable, that can straightforwardly be used to model the commonly observed time-variation in the betas. The discussion of these new measures and methods is accompanied by an empirical illustration using high-frequency data of the IBM incorporation and of the DJIA index
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